Sharif Digital Repository

The Gaussian mixture probability hypothesis density (GM-PHD) filter is a closed form approximation of the multi-target Bayes filter which can overcome most multitarget tracking problems. Limited field of view, decreasing cost of cameras, and advances of using multi-camera induce us to use large-scale camera networks. In this paper, a multihuman tracking framework using the PHD filter in a distributed camera network is proposed. Each camera tracks objects locally with PHD filter and a track-after-detect scheme and its estimates of targets are sent to neighboring nodes. Then each camera fuses its local estimates with it's neighbors. The proposed method is evaluated on the public PETS2009... 

The probability hypothesis density (PHD) filter has been recently introduced by Mahler as a relief for the intractable computation of the optimal Bayesian multi-target filtering. It propagates the posterior intensity of the random finite set (RFS) of targets in time. Despite serving as a powerful decluttering algorithm, PHD filter still has the problem of large variance of the estimated expected number of targets. The cardinalized PHD (CPHD) filter overcomes this problem through jointly propagating the posterior intensity and the posterior cardinality distribution. Unfortunately, the particle filter implementation of the CPHD filter suffers from lack of an efficient method for boosting its...